AbstractIn this paper, we presented a novel adaptive traffic control strategy for urban signalized interchanges based on traditional sensors and emerging connected and automated vehicle (CAV) technologies. The signalized interchanges in this paper refer to those controlled by one traffic signal controller while vehicles must cross two or more stop lines to cross. Examples included diamond interchanges (DIs), diverging diamond interchanges (DDIs), and single-point urban interchanges (SPUIs). With the expansion of urban areas, such interchanges are increasingly common and often become mobility bottlenecks. The traffic signal optimization in this paper was derived from the cumulative vehicle counting curves (A-D curves). An assumption of the A-D curves for control delay estimation is that vehicles are no longer restricted once they cross the stop line. However, at a signalized interchange, vehicles may stop multiple times before completely cross. This situation cannot be effectively reflected with the standard cumulative vehicle counting curves. The phasing sequence is also challenging due to the limited space within the interchange. To address these issues, we proposed a new adaptive traffic control framework based on a linear traffic control model, referred to as a phase-time network. The objective of this framework was to dynamically fine-tune control splits and optimize the phasing sequence according to the vehicle arrival counts (from infrastructure sensors) and turning movement ratios (from CAV technologies). The optimization problem was first formulated into a mixed-integer linear programming (MILP) formulation and validated through offline examples. Then, an online search algorithm was presented and evaluated within a microscopic traffic simulation environment. The proposed MILP formulation and algorithm were assessed in both offline and online experiments. The results of all numerical experiments validated the formulation and show promise for real-world implementations.

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